Ant Colony Optimization is a stochastic search method that mimic the social behavior of real ants colonies, which manage to establish the shortest routs to feeding sources and back. Such algorithms have been developed to arrive at near-optimum solutions to large-scale optimization problems, for which traditional mathematical techniques may fail. In this paper on each iteration estimations of the start nodes of the ants are made. Several start strategies are prepared and combined. Benchmark comparisons among the strategies are presented in terms of quality of the results. Based on this comparison analysis, the performance of the algorithm is discussed along with some guidelines for determining the best strategy. The study presents ideas that should be beneficial to both practitioners and researchers involved in solving optimization problems.
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